Measurement correlation and information tracking for a portable device

ABSTRACT

A wearable device may include a housing formed and shaped to affix to a user and a sensor integrated into the housing and to engage a body of the user to take physiological measurements of the user to obtain first physiological data and second physiological data. A processing device located within the housing (or elsewhere, as in a server device) may receive the first physiological data and the second physiological data; analyze the first physiological data to determine a first correlation between the first physiological data and a physiological parameter; analyze the second physiological data to determine a second correlation between the second physiological data and the physiological parameter; and predict a change in a level of the physiological parameter according to a combination of the first correlation and the second correlation.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/117,282, filed Feb. 17, 2015, and of U.S. Provisional Application No.62/192,998, filed Jul. 15, 2015, the entire contents of which areincorporated by this reference.

BACKGROUND

As portable devices and technology continue to expand and develop,individuals are increasingly searching for devices to measure andmonitor various aspects of their lives. For example, wearable fitnessmonitors may enable users to measure how many steps an individual hastaken over a period of time or an amount of time the individual may beactive over a period of time. Smart watches may enable users to executeapplications, receive and send text messages, make phone calls, and soforth. Portable medical devices may enable a user to take medicalmeasurements outside of a hospital environment. For example, a diabeticmay use a portable insulin measurement device to monitor their bloodsugar level. While portable measurement and monitoring devices mayprovide users with rudimentary measurement and monitoring information,the portable measurement and monitoring devices fail to provide userswith meaningful information to enable users to analyze and improve manydesired aspects of their lives.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the disclosure will be apparent from thedetailed description which follows, taken in conjunction with theaccompanying drawings, which together illustrate, by way of example,features of the disclosure in which like components may be labeled withcorresponding numbering; and, wherein:

FIG. 1 is a block diagram of a system for measurement correlation,information tracking and setting baselines for users of user measurementdevices (UMDs) according to one embodiment.

FIG. 2A illustrates a bottom view of the user measurement device (UMD),such as a wearable wristband, that may be used to take measurementsusing one or more sensors according to one embodiment.

FIG. 2B illustrates a schematic view of the UMD according to oneembodiment.

FIG. 2C illustrates a graph correlating bio-impedance spectroscopy withtissue-blood volume reflection and absorption (or tissue bulkabsorption) of an individual that becomes dehydrated mid-way through aworkout.

FIG. 3 is a block diagram of a wearable UMD with a correlator, abaseliner and an alerter according to one embodiment.

FIG. 4 is a block diagram of a networked device that communicates withUMD's according to one embodiment, and that includes the correlator andthe baseliner.

FIGS. 5A and 5B illustrate a group layer representation of a footballteam in a graphical user interface (GUI) according to one embodiment.

FIG. 5C illustrates a detailed individual layer representation of one ofthe quarterbacks in the GUI according to one embodiment.

FIG. 6 illustrates a hydration index comparing a user's currenthydration to a baseline of hydration for the user.

FIG. 7A depicts a base station or a UMD configured to communicate datawith one or more other devices according to one embodiment.

FIG. 7B illustrates a base station or a UMD operable to communicate syncdata to a computing device according to one embodiment.

FIG. 8 is a flow chart of an exemplary method for correlatingphysiological data to predict a change in a physiological parameteraccording to one embodiment.

FIG. 9 is a flow chart of an exemplary method for analyzing historicalphysiological data and environmental data to predict a change in aphysiological parameter according to one embodiment.

FIG. 10 is a flow chart of an exemplary method for setting a baselinefor a physiological parameter based on a most-similar baseline profileof another according to one embodiment.

FIG. 11 is a flow chart of an exemplary method for updating a baselinefor a physiological parameter based on an update to profile informationof a user according to one embodiment.

FIG. 12 is a flow chart of an exemplary method for alerting a userthrough an indicator of a wearable UMD in anticipation of undertaking aphysical activity according to one embodiment.

FIG. 13 illustrates a diagrammatic representation of a machine in theexample form of a computer system within which a set of instructions,for causing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

FIG. 14 illustrates a block diagram of one implementation of a computersystem.

Reference will now be made to the exemplary embodiments illustrated, andspecific language will be used herein to describe the same. It willnevertheless be understood that no limitation of the scope of theinvention is thereby intended.

DETAILED DESCRIPTION

As data becomes increasingly easier to access, individuals increasinglydesire to monitor, collect, and/or analyze various aspects of theirenvironment and/or physiology. For example, a sport or fitnessenthusiast may desire to monitor, collect, and/or analyze variousaspects of the fitness routine (such as their heart rate, workoutintensity, workout duration, and so forth) to determine how to improveand adjust their fitness routine to increase it efficacy. In anotherexample, an asthmatic may desire to monitor, collect, and/or analyzeenvironmental condition information (such as air quality, pollen count,and so forth) to determine and avoid conditions that may aggravate theircondition. However, traditional portable devices or wearable devicesprovide users with limited and incomplete information to monitor,collect, and/or analyze environment or physiology information desired bythe user.

Aspects of the present disclosure address the above noted deficiency byusing a user monitoring system to monitor, collect, and/or analyzephysiological and environmental data and information. The usermonitoring system may include a user measurement device (UMD) tomonitor, collect, and/or analyze desired environmental and/orphysiological aspects of the user and the user's environment. The UMDmay use sensors, stored data, real-time data, received data, and/oralgorithms to monitor, collect, and/or analyze environmental and/orphysiological information related to an individual, a group ofindividuals, or a business.

In one embodiment, a UMD may include a housing formed and shaped toaffix to a user to engage a body. A sensor integrated into the housingcan take physiological measurements of the user to obtain firstphysiological data and second physiological data. A processing devicelocated within the housing (or elsewhere, as in a server device) mayreceive the first physiological data and the second physiological data.The processing device may analyze the first physiological data todetermine a first correlation between the first physiological data and aphysiological parameter and analyze the second physiological data todetermine a second correlation between the second physiological data andthe physiological parameter. The processing device may then predict achange in a level of the physiological parameter according to acombination of the first correlation and the second correlation. In oneexample, the first physiological data may include optical spectroscopylevels, the second physiological data may include skin impedance levels,and the physiological parameter may be hydration, although othercombinations of envisioned as will be discussed. The sensor may be apart of a sensor array including multiple individual sensors.

In another, or related, embodiment an apparatus may include a processingdevice and non-transitory computer-readable medium storing instructionsand data. The processing device may execute the instructions to performa series of functions. In one embodiment, the processing device mayreceive sensor data including physiological data and environmental data.The processing device may further analyze historical physiological dataand environmental data to determine a first correlation between a firstphysiological parameter and a second physiological parameter and asecond correlation between an environmental parameter and the secondphysiological parameter. The processing device may then predict a changein a level of the second physiological parameter of an identified personfor which the physiological data is received based on the firstcorrelation and the second correlation.

In one embodiment, the first physiological parameter may be any or acombination of oxygenation, heart rate, skin temperature, opticalspectroscopy (or tissue-blood volume reflection and absorption),bio-impedance spectroscopy, and blood pressure, for example. Thetissue-blood volume reflection and absorption level may also be referredto as tissue bulk absorption level for ease of explanation. The secondphysiological parameter may be hydration or oxygenation. Theenvironmental data may be any or a combination of ambient temperature,ambient humidity, altitude, geographical location, and time of day, byway of example. The historical physiological data may be of theidentified person or of a group of persons.

In another, or related, embodiment a method may include receiving,during a first period, first physiological data and second physiologicaldata from a sensor engaging a body of a user. The method may furtherinclude analyzing, using at least one processing device, the firstphysiological data to determine a first correlation between the firstphysiological data and a physiological parameter and analyzing thesecond physiological data to determine a second correlation between thesecond physiological data and the physiological parameter. The methodmay further include predicting a change in a level of the physiologicalparameter during a second time period according to a combination of thefirst correlation and the second correlation, wherein the firstphysiological data and the second physiological data may exclude thephysiological parameter to which the correlation is being made. Only byway of example, the first physiological data may include an averagetissue bulk absorption, the second physiological data may include anaverage bio-impedance spectroscopy, and where the physiologicalparameter may be hydration. In this example, when the average tissuebulk absorption decreases and the average skin impedance increase, theprocessing device may predict that the hydration level of the user maydecrease (e.g., the user is becoming dehydrated).

FIG. 1 is a block diagram of a system 100 for measurement correlation,information tracking and setting baselines for users of user measurementdevices (UMDs). The system 100 may include a plurality of UMDs 110 ofdifferent users, for instance of a group of users such as athletes thatcompete separately and/or athletes on the same team or that compete inthe same or similar sport. The system 100 may further include acommunications network 115 over which the UMDs communicate with a hub120 (or base station) and a server 140. The system 100 may include atower 135 such as a cellular tower or other wireless (and/or wired)access source for communication between the hub 120 and the server 140.

The hub 120 may include memory and storage with a database 130 forstoring measurement, environmental and baseline data of the users. Theserver 140, which may be cloud based, may also include memory andstorage to include a database 150 for storing measurement, environmentaland baseline data of the users. Processing to execute measurementcorrelation, information tracking and setting of baselines forphysiological parameters may occur within the UMDs 110, within the hub120 and/or within the server 140, in various embodiments of the presentdisclosure as will be explained in more detail.

FIG. 2A illustrates a bottom view of the UMD 110, such as a wearablewristband, that may be used to take selected measurements using one ormore sensors 212 and a sensor module 216, according to one embodiment.The UMD 110 may also include one or more indicators 218 used to alertthe user of the UMD to adjust hydration, activity levels, or takespecific actions in preparation of an anticipated physical activity. Theindicators 218 may be on the top or the bottom of the UMD depending ontype of indicator, such as a display or light may be on the top and avibrator may be on the bottom of the UMD. The UMD may also be anon-invasive device, such as another banded device such as a headband,an armband, a legband, or an invasive device other type of deviceattachable to (or implantable within) a body of a user to obtainphysiological measurements from the user.

In one embodiment, the one or more sensors 212 may be a bio-impedance ora bio-impedance spectroscopy sensor, an accelerometer, a threedimensional (3D) accelerometer, a gyroscope, a light sensor, an opticalsensor, an optical spectroscopy sensor, a heart rate monitor, a bloodpressure sensor, a pulse oximeter, and so forth. The sensor module 216may receive measurement information from the one or more sensors 212 andanalyze the measurement information to determine selected physiologicalinformation and/or medical information, such as a hydration level of theuser, cardiac information of the user (e.g., blood pressure or heartrate), a blood oxygen level of the user, a tissue bulk absorption and soforth.

FIG. 2B illustrates a schematic view of the UMD 110 according to oneembodiment. The UMD 110 may include the indicators 218, a sensor array222 (to include at least the sensor 212 as in FIG. 2A), a processingdevice 255, a communications interface 260, an antenna 262 coupled withthe communications interface 260, external sensors 264, and accompanyingantenna(s) 266. In one example, the sensor array 222 may include one ormore physiological sensors to take physiological measurements (e.g.,measurements related to the body of the individual or animal). Thesensor array 222 may include one or more sensors to engage a user of theUMD to take measurements. In various examples, the sensor array 222 mayinclude, without limitation: a bio-impedance spectroscopy sensor 268 (orsimply impedance sensor 268), an optical sensor 270, anelectrocardiogram (ECG) sensor 272, a temperature sensor 274 (such as athermostat or thermistor), an accelerometer 276, a sweat rate sensor 278and so forth. The temperature sensor 274 may measure a temperature ofskin, of a core temperature of a user, or both. The sweat rate sensor278 may measure a rate at which a user perspires to lose sweat.

With reference to FIG. 2C, bio-impedance (shown as the y-axis), whenmeasured by the bio-impedance spectroscopy sensor 268, may determine theelectrical impedance or opposition to the flow of an electric currentthrough body tissues at a range of frequencies. This is to bedistinguished from doing a simple bio-impedance measurement at one or afew discrete frequencies, which would not yield the abundance ofinformation available from a full spectrum sweep that the bio-impedancespectroscopy sensor 268 performs. This (full spectrum) impedance maythen be used to calculate an estimate of total body water (TBW) of anindividual, which in turn may be used to estimate fat-free body mass,and by difference with body weight, body fat of the individual. As usedherein, bio-impedance generally refers to electrical impedance by virtueof TBW as measured through the skin, which may be correlated withhydration and other physiological parameters.

Furthermore, the optical sensor 270 may perform optical spectroscopy ofa skin of an individual wearing the UMD 110. This optical spectroscopymay also be referred to as tissue-blood volume reflection and absorption(or more simply as tissue bulk absorption) as being a test of how muchlight, when directed at the skin, is absorbed compared to how much isreflected and in what wavelengths of the light spectrum. For example,tissue bulk absorption generally refers to a brightness of a lightsource of a certain wavelength as it appears to the eye, measured as aratio of luminous flux to radiant flux at that wavelength. The skin mayinclude blood, collagen, and other compounds being tested by the opticalspectroscopy. When the skin is tested as that light source in responseto light from the optical sensor 270, the optical sensor may detect howmuch light is being reflected as a volume reflection and absorptionparameter. When most of the light is absorbed, tissue bulk absorption ishigh, which may occur when an individual is dehydrated.

For example, as shown the middle of the graph in FIG. 2C, asbio-impedance spectroscopy goes up over time (such as during exercise),the tissue reflection level detected from the optical sensor goes down,e.g., the tissue bulk absorption increases. In other words,bio-impedance spectroscopy and tissue reflection level are inverselycorrelated with respect to hydration, e.g., bio-impedance goes up andtissue reflection level goes down as a level of hydration decreases.And, similarly, as the individual cools down and/or hydrates, thebio-impedance level comes back down and the tissue reflection level goesback up (and tissue bulk absorption decreases), as seen at theright-most part of the graph in FIG. 2C.

With reference to the other sensors of the sensor array 222, one couldalso expect skin temperature to increase as hydration decreases due tothe body's inability to cool itself as dehydration sets in. Furthermore,the individual's heart rate may also increase as dehydration puts stresson the body. In this way, the data from the different sensors of thesensor array 222 may inter-correlate and may do so in ways thatgeneralize over a population at higher granularities, and may do so inways that are more customized to individuals at lower granularities.

With further reference to FIG. 2B, the processing device 255 may includea processor, a memory storage device, an analog-to-digital converter,and/or a digital-to-analog converter. In one example, the processingdevice 255 may be coupled to the communication interface 260 tocommunicate data with other devices using the antenna 262. The antenna262 may be configured to communicate on a wireless network and/or acellular network such as the communications network 115 (see FIG. 14).In another example, the processing device 255 may be coupled to one ormore external sensors. The external sensors 264 may be sensors that takemeasurements external to the user, such as non-physiologicalmeasurements or non-direct engagement measurements of the user,including environmental parameters, temperature, humidity, altitude,wind and the like. The external sensors 264 may include, or beintegrated with, a global positioning system (GPS) device, atriangulation device, a humidity sensor, an altimeter, and so forth.

In other embodiments, the processing device 255 or a portion of theprocessing device 255 may be located elsewhere such as in the hub 120(or base station), a communication switch or a server 140, for example.Such examples will be discussed in more detail with reference to FIG. 4.

FIG. 3 is a block diagram of the wearable UMD 110 with a correlator, abaseliner and an alerter according to one embodiment. The wearable UMD110 may include, without limitation, one or more physiological sensor(s)302, one or more activity sensor(s) 304, a processor 303, a memorydevice 308, a display 380, a radio frequency (RF) circuit 390 and anantenna 392 coupled to the RF circuit 390. The RF circuit 390 maycommunicate with the communications network 115, the hub 120 andoptionally with other wireless devices such as UMDs 110 of other users,as shown in FIG. 1.

The processor 303 may include a first sensor interface 307 for receivingsensor data from the physiological sensor(s) 302, a second sensorinterface 309 for receiving sensor data from the activity sensor(s) 304,and a processing component 311. The processing component 311 in turn mayinclude a correlator 313, a baseliner 315 and/or an alerter 317. Thememory device 308 may further include, without limitation, a sensormodule 316, physiological data 324, environmental data 326, activitydata 328 and profile data 330.

The wearable UMD 110 may include the sensor array 222 (FIG. 2B) with twoor more sensors. In the depicted embodiment, the wearable UMD 110 mayinclude one or more physiological sensors 302 and one or more activitysensors 304. In some instances, the activity sensors 304 may bephysiological sensors. That is, in some embodiment, the activity levelmay be determined from one or more physiological measurements.

A physiological measurement may be any measurement related to a livingbody, such as a human's body or an animal's body. The physiologicalmeasurement is a measurement made to assess body functions.Physiological measurements may be simple, such as the measurement ofbody or skin temperature, or they may be more complicated, for examplemeasuring how well the heart is functioning by taking an ECG(electrocardiograph). Physiological measurements may also include motionand/or movement of the body. In some cases, these physiologicalmeasurements may be taken as an aggregate, e.g., as physiological data,with which to correlate to other physiological measurements, aphysiological parameter, and/or an environmental parameter.

Herein, a parameter may generally be considered a measurable quantity(such as heart rate, tissue-blood volume reflection and absorption,temperature, altitude, and oxygen level, as just a few examples). Whenmeasurements of parameters are taken in the aggregate, the measurementsmay form data which may be analyzed and correlated to other data orparameters, to identify trends or to identify when meeting (orexceeding) certain thresholds that trigger alerts or other actions andthe like.

The physiological sensors 302 may include a pulse oximeter sensor, anelectrocardiography (ECG) sensor, a fluid level sensor, an oxygensaturation sensor, a body temperature sensor (e.g., a skin temperaturesensor), a skin temperature sensor, a plethysmograph sensor, arespiration sensor, a breath sensor, a cardiac sensor (e.g., a bloodpressure sensor, a heartrate sensor, a cardiac stress sensor, or thelike), an impedance sensor (e.g., bio-impedance spectroscopy sensor), anoptical sensor, a spectrographic sensor.

The activity sensors 304 may be any of the physiological sensorsdescribed above, but in some cases, the activity sensors 304 areNewtonian sensors, such as, for example, a gyroscope sensor, a vibrationsensor, an accelerometer sensor (e.g., a sensor that measuresacceleration and de-acceleration), a three dimensional (3D)accelerometer sensor (e.g., sensors that measure the acceleration andde-acceleration and the direction of such acceleration andde-acceleration), a force sensor, a pedometer, a strain gauge, amagnetometer, and a geomagnetic field sensor that may be used foractivity level measurements; whereas the physiological sensors 302 maybe used for specific physiological measurements.

In another embodiment, the physiological sensors 302 and activitysensors 304 may be categorized into physiological sensors, environmentalsensors and Newtonian sensors. The one or more physiological sensors maybe a pulse oximeter sensor, an electrocardiography (ECG) sensor, a fluidlevel sensor, an oxygen saturation sensor, a body temperature sensor, anambient temperature sensor, a plethysmograph sensor, a respirationsensor, a breath sensor, a cardiac sensor, a heartrate sensor, animpedance sensor, an optical sensor, a spectrographic sensor, or thelike. The one or more environmental sensors may be, for example, ahumidity sensor, an ambient temperature sensor, an altitude sensor, abarometer, a global positioning system (GPS) sensor, a triangulationsensor, a location sensor, or the like. The one or more Newtoniansensors may be, for example, a gyroscope sensor, a vibration sensor, anaccelerometer sensor, a three dimensional (3D) accelerometer sensor, aforce sensor, a pedometer, a strain gauge, a magnetometer, a geomagneticfield sensor, or the like. Alternatively, other types of sensors may beused to measure physiological measurements, including measurements todetermine activity levels of a person wearing the UMD. Furthermore,environmental data may be obtained from other sources such as throughthe network 115 from sources reachable in the cloud or online.

The first sensor interface 307 may be coupled with the one or morephysiological sensors 302 and a second sensor interface 309 may becoupled with the one or more activity sensors 304. The processingcomponent 311 may be operable to execute one or more instructions storedin the memory device 308, which may be coupled with the processor 303.In some cases the processing component 311 and memory device 308 may belocated on a common substrate or on a same integrated circuit die.Alternatively, the components described herein may be integrated in oneor more integrated circuits as would be appreciated by one having thebenefit of this disclosure. The memory device 308 may be any type ofmemory device, including non-volatile memory, volatile memory, or thelike. Although not separately illustrated, the memory device may be oneor more types of memory configured in various types of memoryhierarchies.

The memory device 308 may store physiological data 324, such as currentand past physiological measurements, as well as profile data 330,including user profile data, bibliographic data, demographic data, andthe like. The physiological data 324, and in some cases the profile data330, may also include processed data regarding the measurements, such asstatistical information regarding the measurements, as well as dataderived from the measurements, such as predictive indicators, results,and/or recommendations.

The profile data 330 may also include information connected to userprofiles of the users that wear the UMDs 110, such as gender, age,weight, health, fitness level and family health histories. The profiledata 330 may also be linked to various physiological data 324 andactivity data 328 and tracked over time for the users. The profile data330 may further include baselines of physiological parameters forrespective users. In one example, the baselines are of a heart rate, ablood pressure, bio-impedance spectroscopy, skin temperature, tissuebulk absorption, oxygen levels, hydration levels, electrolyte levels andso forth. When the baselines are included with the user profiles, theuser profiles may be referred to as baseline profiles for the respectiveusers.

The memory device 308 may also store activity data 328. The activitydata 328 may be current and past measurements, as well predictive datafor predictive modeling of activity levels. The memory device 308 maystore instructions of the sensor module 316 and instructions and datarelated to the correlator 313, the baseliner 315 and the alerter 317,which perform various operations described below.

In particular, the sensor module 316 may perform operations to controlthe physiological sensors 302 and activity sensors 304, such as when toturn them on and off, when to take a measurement, how many measurementsto take, how often to perform measurements, etc. For example, the sensormodule 316 may be programmed to measure a set of physiologicalmeasurements according to a default pattern or other adaptive patternsto adjust when and how often to take certain types of measurements. Themeasurements may be stored as the physiological data 324, theenvironment data 326, and the activity data 328, and some of them mayalso be integrated as a part of the profile data 330, as discussed.

In the depicted embodiment, the processing element 307 (e.g., one ormore processor cores, a digital signal processor, or the like) executesthe instructions of the sensor module 316 and those related to thecorrelator 313, the baseliner 315, the alerter 317 and possibly othermodules or routines. Alternatively, the operations of the sensor module316 and the correlator 313, the baseliner 315 and the alerter 317 may beintegrated into an operating system that is executed by the processor303. In one embodiment, the processing component 311 measures aphysiological measurement via the first sensor interface 307. Theprocessing component 311 may measure an amount of activity of thewearable UMD 110 via the second sensor interface 309. The amount ofactivity could be movement or motion of the wearable UMD 110 (e.g., bytracking location), as well as other measurements indicative of theactivity level of a user, such as heart rate, body temperature, tissuebulk absorption, or the like.

In one embodiment, the activity sensors 304 may include a hardwaremotion sensor to measure at least one of movement or motion of thewearable UMD 110. The processing component 311 may determine the amountof activity based the movement or motion of the wearable UMD 110. Thehardware motion sensor may be an accelerometer sensor, a gyroscopesensor, a magnetometer, a GPS sensor, a location sensor, a vibrationsensor, a 3D accelerometer sensor, a force sensor, a pedometer, a straingauge, a magnetometer, and a geomagnetic field sensor.

The processor 303 may further execute instructions to facilitateoperations of the UMD 110 that receive, store and analyze measurementdata, environmental data and profile data. The indicator(s) 318 mayinclude one or more of a light, a display, a speaker, a vibrator, and atouch display, useable to alert the user to take actions in response totrending levels of physiological parameters during or after physicalactivity and/or prepare for undertaking anticipated physical activity.

In some embodiments, for example, the correlator 313 may analyzemeasurement data to correlate physiological data and/or environmentaldata with a physiological parameter of interest (such as hydration oroxygenation) in order to predict a change in a level of thephysiological parameter of interest. Such prediction may enable timelyand accurate recommendations to a user in terms of hydrating, adjustingeffort levels or other specific actions to address a trend or a changein the physiological parameter. The recommendations may be displayed inthe display 380, sent via an alert through one of the indictor(s) 318 ordisplayed in another device such as a smart phone or tablet or othercomputing device.

In another embodiment, the correlator 313 may also track and analyzeactivity data of the user related to physiological or determinedparameters (such as heart rate, oxygenation, tissue bulk absorption,hydration, and the like), related to location and type of activity (suchas activity levels associated with being at the gym, riding a bike,attending class, working at a desk, sleeping, or driving in traffic, andthe like) and/or related to scheduling information (such as appointmentson a calendar, invites received from friends, or messages related totravel and/or activity plans, and the like). Through this analysis, theUMD 110 may track activity data over time, intelligently andcontinuously (or periodically) analyze all of this information, andalert the user through the indicator(s) 318 to take a specific action ata proper time before a start of the physical activity, as will beexplained in more detail. The proper time may include to hydrate extrain the hours before physical activity and to eat at least two hoursbefore any physical activity, or other such timing that may be generalto most users, or customized to a training or nutrition routine of aspecific user.

The alerter 317 may decide the most appropriate timing and mode ofalert, whether through one of the indicator(s) 318, the display 380 oranother device such as a smart phone, tablet or the like. The type ofindicator used to alert the user may also be customized to or by theuser.

In one embodiment, the correlator 313 may determine a correlationbetween different data points or data sets of the input data (such asdata collected from different sensors, devices, or obtained from thecloud or online). The correlator 313 may determine different types ofcorrelations of the data points or data sets. In one example, thecorrelator 313 may execute a Pearson product moment correlationcoefficient algorithm to measure the extent to which two variables ofinput data may be related. In another example, the correlator 313 maydetermine relations between variables of input data based on asimilarity of rankings of different data points. In another example, thecorrelator 313 may use a multiple regression algorithm to determine acorrelation between a data set or a data point that may be defined as adependent variable and one or more other data sets or other data pointsdefined as independent variables. In another example, the correlator 313may determine a correlation between different categories or informationtypes in the input data.

In further examples, when the correlator 313 determines a correlationbetween the different data points or data sets, the correlator 313 mayuse the correlation information to predict when a first event orcondition may occur based on a second event or condition occurring. Inanother example, when the correlator 313 determines a correlationbetween the different data points or data sets, the correlator 313 mayuse the correlation information to determine a diagnosis or result data.In another example, when the correlator 313 determines a correlationbetween the different data points or data sets, the correlator 313 mayuse the correlation information to determine a cause of a conditionand/or event.

Additionally, or alternatively, the correlator 313 may determine acorrelation between physiological data and environmental data. Forexample, the input data may include hydration level data (physiologicaldata) and ambient temperature data (environmental data). In thisexample, the correlator 313 may identify a correlation between when theambient temperature increases and a decrease in a hydration level of auser. The correlator 313 may identify the correlation between theambient temperature and the hydration level by using a regressionalgorithm with the ambient temperature as an independent variable andthe hydration level as a dependent variable. When the correlator 313 hasidentified the correlation between the ambient temperature and thehydration level, the correlator 313 may predict a change in a hydrationlevel of a user or a rate of change of a hydration level of a user basedon the ambient temperature.

Additionally, or alternatively, the correlator 313 may determine acorrelation between an altitude level and an oxygenation level of auser. For example, the correlator 313 may determine a correlationbetween an increase in the altitude level and a decrease in theoxygenation level of the user. When the correlator 313 determines thecorrelation between the altitude level and the oxygenation level, thecorrelator 313 may predict a change in the oxygenation level of userbased on the altitude level at which the user is currently.

The preceding examples are intended for purposes of illustration and arenot intended to be limiting. The correlator 313 may identify acorrelation between various data points, data sets, and/or data types.After having a correlation that informs, for example, the hydrationlevel and/or oxygenation level of the user, and further in considerationof a present activity level of the user, the alerter 317 may alert theuser at the proper time when to hydrate or how to moderate activitylevels for healthy functioning of the body and its organs, or formaximizing performance of an athlete.

In a further example, the correlator 313 may identify a correlationbetween location information and physiological data of a user. Forexample, the correlator 313 may determine a location of a user for at aperiod of time, such as by using GPS sensor data or triangulation sensordata. In this example, the correlator 313 may receive physiologicalmeasurement data (such as heart rate measurement data, opticalspectroscopy data, hydration level measurement data, blood pressuremeasurement data, and so forth). The correlator 313 may correlate thelocation of the user with the physiological measurement data to increasean accuracy of data analysis, a diagnosis, or result data and/or provideadditional details regarding a cause of physiological measurements ortrends.

In one example, the correlator 313 may determine that a user is at workin an office location. When the correlator 313 detects an increase in aheart rate or a blood pressure of a user, the correlator 313 maycorrelate heart rate or blood pressure data with the locationinformation to determine a cause of the increase in heart rate or bloodpressure. For example, when a heart rate or blood pressure of anindividual increases while at a work in an office, the correlator 313may determine that the heart rate or blood pressure increase may be dueto psychological causes (such as stress) rather than physiologicalcauses (such as exercising or working out) because the user is at alocation where an individual is not likely to physically exert himselfor herself.

In a further example, the correlator 313 may use a multiple regressionalgorithm to determine a correlation between multiple physiologicaland/or environmental data points or data sets. For example, thecorrelator 313 may receive heart rate data, skin temperature,bio-impedance spectroscopy data, tissue bulk absorption and hydrationlevel data of a user. In this example, the correlator 313 may determinea correlation between these types of physiological data and a hydrationlevel of the individual. For example, the physiological data could befrom optical spectroscopy (tissue-blood volume reflection andabsorption, or more simply, tissue bulk absorption) and/or bio-impedancespectroscopy data. The correlator 313 may then determine that as thebio-impedance of an individual increases and tissue bulk absorptiondecreases, the hydration level of the individual may decrease.

Additionally, or alternatively, the correlator 313 may filter out acorrelation determination (e.g., a determination that data points ordata sets may be correlated) when a correlation level is below athreshold level. For example, when the correlator 313 determines thatthere may be a 30 percent correlation between a skin temperature or abio-impedance spectroscopy level of an individual and a hydration levelof an individual, the correlator 313 may filter out or disregard thecorrelation information when determining a cause of a condition orevent, a result of the data, or a diagnosis or prediction.

Additionally, or alternatively, the correlator 313 may discount orweight a correlation determination based on the correlation level of thecorrelation determination. For example, when the correlator 313determines that there may only be a 30 percent correlation between abio-impedance level of an individual and a hydration level of anindividual, the correlator 313 may discount or assign a lower weight tothe correlation determination (relative to a higher correlationpercentage such as 90 percent) when determining a cause of a conditionor event, a result of the data, or a diagnosis.

Additionally, or alternatively, the correlator 313 may assign weights todifferent factors, such as: physiological data (e.g., different types orqualities of physiological parameters), environmental data (e.g.,different types or quality of environmental parameters), time of day,and so forth. Quality of data may reference a signal-to-noise (SNR)ratio of that data, where the higher the SNR, the higher the weight maybe applied to the data. In regards to data type, in one example, thecorrelator 313 may assign a first weight to hydration level data of anindividual and a second weight to heart rate data of an individual whendetermining a performance level of an individual. In this example, whendetermining a performance level, the correlator 313 may assign a higherweight to the hydration level data relative to the heart rate data, forexample.

The correlator 313 may additionally, or alternatively, use predeterminedweights for the different physiological and/or environmental data. Inanother example, the correlator 313 may receive user defined orpredefined weights from an input device indicating the weights for thedifferent physiological and/or environmental data. In another example,the correlator 313 may determine the weights to assign to the differentphysiological and/or environmental data based on correlation levels ofthe different physiological and/or environmental data. For example, whena correlation level between a humidity level and a heart rate of anindividual may be relatively low over a threshold period of time and/orunder a threshold number of different conditions, the correlator 313 mayassign a low weight to humidity level data when determining a cause of achange in heart rate of a user.

In one example, the correlator 313 may assign different weights tophysiological measurements based on environmental data. For example,based on a location of an individual, the correlator 313 may assign afirst weight to a heart rate measurement and a second weight to arespiration sensor measurement.

In another example, the correlator 313 may assign weights to differentcauses, diagnosis, or results, such as: an exertion level (e.g., workingout or sleeping), a stress level, an amount of time a user sleeps eachday, and so forth.

Additionally, or alternatively, the correlator 313 may use environmentaldata to determine a cause of a physiological diagnosis. For example,when a user is located at a fitness facility working out, the correlator313 may increase a weight for physical exertion diagnosis as a cause ofphysiological measurements (such as an increase in a heart rate ordecrease in a hydration level of a user). In another example, when auser is located at home in bed resting or sleeping, the correlator 313may correlate a location of the user with physiological measurements ofthe user. In this example, the correlator 313 may determine that adecrease in heart rate may be due to an individual going to sleep when auser is located in their bedroom for a threshold period of time.

The correlator 313 may further combine environmental data used in thisway with a correlation determination between a physiological parameterand physiological data such as past measurements of the user or of agroup of users.

The correlator 313 may track, sort and/or filter input data. The inputdata may include: user schedule information, such as a daily schedule ofthe user; survey information, such as information received from surveysof individuals; research information, such as clinical researchinformation or academic research information associated with one or moremeasurements of the UMD; and so forth.

The correlator 313 may use location-based tracking and/or schedulinginformation of the user in determining an expected or probable activityof a user. For example, when a user is a member of a sports team, theuser's schedule may include practice schedule information and/or gameschedule information. In this example, the correlator 313 may use theschedule information to anticipate that the user may be participating inphysical activity and the alerter 317 provide recommendations to theuser based on the anticipated physical activity. For example, thecorrelator 313 may determine that the user may be practicing in twohours, may determine a current hydration level of the user, and maycommunicate a recommendation (such as via a sensory indicator of theUMD) to increase the hydration level of the user. A sensory indicator,such as one of the indicator(s) 318, may include: a visual indicationdevice, such as a display; an auditory indication device, such as aspeaker; and/or touch indication device, such as a vibrator.

In another example, the correlator 313 may use the schedulinginformation in correlation with a location of the user to determine anexpected or probable activity. For example, the scheduling informationmay indicate that the user may be scheduled to attend a lecture at aphysical fitness facility and the correlator 313 may adjust a locationbased recommendation in view of the scheduling information. In thisexample, while the correlator 313 may typically recommend increasing ahydration level of the user in anticipation of physical activity basedon the location information (e.g., the physical fitness facility), thecorrelator 313 may adjust the recommendation in view of the schedulinginformation that the user may be attending a lecture rather than workingout.

Additionally, or alternatively, the correlator 313 may track and updateactivity levels of users and correlate these levels with locations ofthe users over time. For example, the GPS sensor of the UMD 110 mayindicate that the user usually works out at the gym on Monday, Wednesdayand Friday at 7 a.m. and goes on a long bike ride on Saturday, usuallystarting about 8:30 a.m. Although these activities may not be availablewithin the scheduling information or data of the UMD 110 (or othertethered device), the correlator 313 may execute machine learning to addto a user's activity data these events that normally occur.

Furthermore, the UMD 110 may distinguish the activity based on analysisof the context of physiological measurements, environmental data anduser profile data. For example, the correlator 313 may determine thatthe user is at the gym on certain days because of the location of thegym, and may correlate an increase in heart rate with each visit,strengthening the probability that the user is working out at that gymlocation. Similarly, the GPS sensor of the correlator 313 may track theuser's bike ride on Saturday and note 40-60 miles routes during periodsof increased heart rate. The correlator 313 may exclude driving as theactivity based on one or more factors, such as a speed of travel beingfar below that of the speed limit for automobiles and/or a skinimpedance or temperature that is higher than when the user is driving.The correlator 313 may also track non-physical activity events such asperiods of time the user normally sleeps, is at work, or is at leisuresuch as at a resort, fishing or on vacation. All of these activities maybe learned and programmed into the UMD over time as part of the user'sactivity data.

The UMD 110 may store historical or previous input data of the user. Inone example, the correlator 313 may store the historical information onthe memory device 308 of the UMD 110. In another example, the correlator313 may use the communication interface 260 (FIG. 2B) to store theinformation on a memory device coupled to or in communication with theUMD, such as a cloud-based storage device or a memory device of anothercomputing device. In another example, the correlator 313 may be part ofa cloud-based system or the other computing device, as will be discussedin more detail with reference to FIG. 4.

The correlator 318 may filter and/or sort input data. In one example,the correlator 318 may receive a filter or sort command from the UMD oran input device to filter and/or sort the input data. In anotherexample, the filter or sort command may include filter parameters and/orsort parameters. The filter parameters and/or sort parameters mayinclude: a time of day, a day of the week, group information, individualinformation, a measurement type, a measurement duration, an activitytype, profile information, injury information, performance levelinformation, and so forth.

In another example, the correlator 313 may sort and/or filter the inputdata based on a trending of input data. For example, the correlator 313may sort input data that may be trending in an increasing direction or adecreasing direction and may sort the input data based on the trending.In this example, different measurements for a user may be trending indifferent directions, such as a hydration level of a user may betrending towards a dehydrated level and an activity level of the usermay be stable or stagnant. The correlator 313 may sort input data todisplay hydration level trending because the user may trending towardsdehydration while filtering out, or disregarding, the activity levelinformation.

In one example, the correlator 313 may sort or filter the input data ona group level. In another example, the correlator 313 may sort or filterthe input data on an individual level.

In another embodiment, the baseliner 315 may receive profile informationfrom a new user to include any or a combination of gender, age, weight,health, fitness level, and family health histories. The health andfitness levels of the user may be based at least in part onphysiological measurements received from the physiological sensor(s) 302and the activity data received from the activity sensors 304. Thebaseliner 315 may then identify, from a plurality of baseline profilesof other users (e.g., a group of users), a baseline profile that ismost-similar to the user profile based on a correlation between the userprofile information and baseline profile information. The baseliner 315may then be able to set a baseline against which to judge measurementsof a physiological parameter of the user that corresponds to levels ofthe physiological parameter of an individual with the baseline profilethat is most-similar. In an alternate embodiment, the baseline profilethat is most-similar to the user profile is identified from anaggregated baseline profile for a plurality of individuals correspondingto the plurality of baseline profiles.

Alternatively, or additionally, the most-similar profiles may look atphysiological and/or environmental measurements of the individual ascompared to the group. For example, the user may be most similar toanother individual because they both react physiologically similarly tohot temperatures outside. In another example, the user may have asimilar dehydration profile to the most-similar profile, meaning, whenthe user works out the user may reach a dehydration level at a certainpoint in time that substantially matches the timing of the most-similarprofile.

The UMD 110 may further receive survey information and/or researchinformation from an input device with which to build or add to the userand/or baseline profiles. For example, the UMD 110 may receive surveyinformation that includes: gender information, age information, physicalweight information, general health information, family information,fitness level information, and so forth. In one example, the correlator313 may determine a correlation between the survey information and userinput data. For example, the correlator 313 may correlate the age,weight, fitness level, and general health level of a user with surveyinformation from other individuals to determine a correlation betweenthe survey information for the individual and the other individuals. Inthis example, the baseliner 315 may set a baseline for a measurement ofthe UMD 110 for the individual based on baselines for the otherindividuals with the same or similar survey information.

In another example, the correlator 313 may correlate the userinformation with research information (such as research papers, clinicalstudies, and so forth). For example, the UMD may retrieve researchinformation related to a physiological parameter, the correlator 313 maythen correlate the research information with measurements of thephysiological parameter of the user to generate a research correlation.The baseliner 315 may then adjust the baseline set for the user relatedto the physiological parameter in response to the research correlation.

FIG. 4 is a block diagram of a networked device 400 that communicateswith UMD's according to various examples. In one embodiment, the networkdevice 400 may be the hub (or base station) 120 illustrated in FIG. 1.In another embodiment, the network device 400 may be a server devicesuch as the server 140 illustrated in FIG. 1. As such, the networkeddevice 400 may include a communication interface 460 which with tocommunicate over the communications network 115. In some cases, thecommunication may be with the help of an RF circuit 490 and antenna 490,to communicate wirelessly, e.g., as may be used by the hub 120 (or abase station or a switch or the like that) at least in someimplementations.

In these examples, the components of the networked device 400 arelargely the same as those discussed above with reference to the UMD 110of FIG. 3, with the exception of the sensors 302 and 304 andindicator(s) 318. Sensor data may be sent from the UMDs to the networkeddevice over the network 115 and through the communications interface460. Furthermore, any alerts or information for the user may be sentback to the UMD to initiate one of the indicator(s) 318 or provideinformation to the user through the display 380. The functions andcapabilities of the UMD 110 of FIG. 3 may generally be replicated orenhanced by the functions and capabilities of the networked device 400in terms of processing power, data analytics, performing correlations,predictions, analyzing and setting baselines, and other algorithmicwork.

Accordingly, in one embodiment, the network device 400 may furtherinclude, without limitation, a processor 403, a memory device 408, and adisplay 480. The processor 403 may include a processing element 411 thatmay have a correlator 413, a baseliner 415 and an alerter 417. Thememory device may include a sensor module 416, physiological data 424,environmental data 426, activity data 428 and profile data of andrelated to the users.

In one embodiment, the physiological sensors 302 and the activitysensors 304 of the UMD 110 may generate measurements, information anddata that is stored in the memory device 308 of the UMD 110 as alreadydiscussed. In another embodiment, however, the UMD 110 may send thisdata and information to the networked device 400 to be stored as thephysiological data 424, the environmental data 426, the activity data428 and/or the profile data 430. The user profiles and, optionally, thebaseline profiles of the users may also be stored with the profile data430. And, as discussed previously, historical information may be storedby the networked device 400 that may help the networked device 400 withmachine learning and to perform more intense processing and statisticalanalysis that may be required to implement the processes and strategiesdisclosed herein.

To do so, for example, the processing element 411 may function largelythe same as the processing component 311 discussed with reference to theUMD 110 of FIG. 3, but with perhaps greater processing power and speed.In other words, the correlator 413 may function similarly to thecorrelator 313 upon receipt of the physiological, environmental and userdata from the UMD 110 and/or from other sources. Those other sources mayinclude cloud or Internet servers that provide weather, altitude,geographic and location information, calendar and schedulinginformation, and other such information or data. Some of these sourcesmay include other devices of the user such as a smart phone, tablet, ascale, a refractometer, a plasma osmolality device or other computingdevice that may or may not be tethered with the UMD 110, in alternativeembodiments.

Furthermore, the baseliner 415 may function similarly to the baseliner315 of the UMD 110 of FIG. 3 based on sensor data and other informationreceived from the UMD 110 or the other sources. Additionally, thealerter 417 may function similarly to the alerter 317 of the UMD 110 ofFIG. 3, based on sensor data and other information received from the UMD110 or other sources.

In one embodiment, the UMD 110 may generate additional physiological andenvironmental data from on-going measurements. The user may also enternew information that may change the user's profile, such as a new age orposition on a sport's team, and this information may also be sent by theUMD 110 to the networked device 400. When a baseline is set by the UMD110 for a user as discussed previously, this baseline may also be sentto the networked device 400 where baselines for all users may be kept upto date, for purposes of tracking those users as well as providing datafrom which an initial baseline may be set for a physiological parameterof a newly added user. Furthermore, or alternatively, the baseline maybe set by the baseliner 415 of the networked device 400 and sent to theUMD of the corresponding user against which new physiologicalmeasurements may be compared and/or correlated locally by the UMD.

Furthermore, any updates to a user profile, new physiological and/orenvironmental measurements may be sent to the networked device 400 bythe UMD 110. As this type of information is updated, the networkeddevice 400 may also update the baseline and/or baseline profiles of theusers as disclosed herein. Any such updated baseline may be sent to thecorresponding UMD 110 of the correct user, which UMD 110 may then updatethe baseline for that user locally.

FIGS. 5A and 5B illustrate a group layer representation 500 of afootball team in a graphical user interface (GUI) or other displayaccording to one embodiment. Using the example of a football team, thegroup layer representation 500 may include, but not be limited to, aquarterback section 502, a tight end section 504, a running back section508, a wide receiver section 512, and an offensive lineman section 514,each with varying amounts of profile information, physiological data,activity level data, and trending values of physiological parameter(s)and activity levels. This information may be provided through a GUI,touch screen or other display of any computing device including a smartphone, a tablet, a laptop, a desktop, or a server device, or any suchdevice accessible by a coach, a trainer, medical specialists and/or theusers of the UMDs disclosed herein. While hydration and activity levelare the two physiological parameters of focus in FIGS. 5A, 5B, 5C and 6,others are envisioned and therefore these are provided by way of exampleonly.

In the present example, the quarterback section 502 may includeuser-specific data blocks 520, where for each user, the data block mayinclude profile information 521, a physiological parameter status 522(e.g., of activity level and hydration level), an average percentage 524of hydration and activity level, respectively, a graph 525 of targethydration and activity level, and a battery level 526 for the UMD 110 ofthe user. The graph 525 may, in one embodiment, include curves forhydration and activity levels. A hydration target level and a targetactivity level may be shown at the right of, or otherwise in conjunctionwith, the graph 525.

In the user-specific data block 520, the users may be listed accordingto string (e.g., starting, first string, second string, etc.),alphabetically or based on other criteria. In FIG. 5A, Brian Fering islisted first followed by Wilson Tart and so forth through Alan Swissa.Each user's data block may be selectable to obtain more information forthe specific user. For example, when the data block 520 (or portionthereof) for Brian Fering is selected, the screen of FIG. 5C may pop upto show additional details and statistics on Brian Fering (discussedbelow).

The quarterback section 502 may also include one or more trending blocksto show trending information for the quarterbacks as a whole. In thepresent example, the trending blocks may include a hydration trendingblock 530 and an activity level trending block 534, although otherscould be provided based on physiological parameters the coaches, trainerand/or users want to track. Each trending block may provide a seasonlevel (and how that compares with the previous season), today's level(and how that compares with the season level or a previous day level),and this week's level (and how that compares with the season level or aprevious week). Each of the “season,” “today,” and the “week” may beselectable to be able to see trending data for, e.g., hydration and/oractivity level, at different granularities. This information may behelpful to a coach, a trainer, team doctor or therapist and to theindividual users to track progress over the season and during shorterperiods of time, as well as to help set baselines for physiologicalparameters for new athletes to the team, as already discussed, or forpreviously injured or ill athletes that are returning to practice orhigher levels of physical activity.

With continued reference to FIG. 5A, the tight end section 504 mayinclude similar information to the quarterbacks, but in this case (andto save room), there may be less profile information. Accordingly, thetight end section 504 may include an average percentage 540 forhydration and activity level, respectively, over the season, eachathlete's name 542, a UMD identifier 544, and a battery level 546 forthe UMD of each respective tight end. The tight end section 504 may alsoinclude trending blocks including a hydration trending block 550 and anactivity level trending block 554 with similar options as discussed withreference to the quarterback section 502. Each tight end in the tightend section 504 may be selectable (e.g., through selection of the nameof the tight end) to bring up more detail on the athlete such as shownin FIG. 5C.

With further reference to FIG. 5B, the running back section 508 may alsoinclude trending blocks for physiological (and other related) parameterssuch as activity level. In the present example, the trending blocksinclude a hydration trending block 560 and an activity level trendingblock 564 to shown similar information as discussed with reference tocorresponding trending blocks in the quarterback section 502.

Other positions may include profile, physiological and other data aswell, including, for example, in the wide receiver section 512 and theoffensive lineman section 514. In FIG. 5B, the wide receiver section 512may include a hydration trending block 570 and an activity leveltrending block 574, with selectable sections to provide granularity toinspect the season, today, or this week's trending data. The offensivelineman section 514 may also include a hydration trending block 580 andan activity level trending block 584, with selectable sections toprovide granularity to inspect the season, today, or this week'strending data. This information may be helpful to a coach, a trainer,medical personnel and/or to the individual users to track progress overthe season and during shorter periods of time, as well as to help setbaselines for physiological parameters for new or returning athletes tothe team, as already discussed.

FIG. 5C illustrates a detailed individual layer representation 538 ofone of the quarterbacks in the quarterback section 502 of FIG. 5A,according to one embodiment. This individual layer representation 538may result from selection of “Brian Fering” in the user-specific datablock 520 for Brian Fering (e.g., selection of Brian Fering's name orother profile information for Brian Fering), and in one embodiment, maybe a pop up window that is displayed in the GUI. The detailed individuallayer representation 538 may include an information settings block 523,to include settings, email, and messages, the latter of which mayfacilitate the user (Brian Fering) communicating regarding his trainingregime, for example, with a coach, trainer and/or medical person.

The detailed individual layer representation 538 may include much of thesame information as displayed in the quarterback section 502 in thegroup layer representation 500 in FIG. 5A, but with some of theinformation and data customized to the selected athlete, e.g., BrianFering. For example, the physiological parameter status 522 may includecurrent levels of physiological parameters, including hydration (shownas “60” currently for Brian Fering), heart rate, core or skintemperature, blood pressure, tissue bulk absorption, bio-impedancespectroscopy and the like. Furthermore, the hydration trending block 530may be customized to Brian Fering's hydration data, and thereforeexclude the rest of the quarterback's hydration data. Similarly, theactivity level trending block 534 may be customized to Brian Fering'sactivity levels, and therefore exclude the rest of the quarterback'sactivity level data.

The graphs on the right in FIGS. 5A and 5C and the graphs of FIG. 5Bshow a layering of the different measurement information which canindicate a correlation between the different measurements. Additionaltrending graphs may be provided to help compare the selected user'sphysiological data to that of the team's. For example, the individuallayer representation 538 may also include a weekly activity trendinggraph 592 and a weekly hydration trending graph 594 (compared to targetvalues) and a comparative team trending graph 596 for both hydration andactivity level. The individual layer representation 538 may furtherinclude a notes section 598 for brief notes about nutrition regimes,workout schedules and adjustments, and medical conditions or reactionsto team or individual training.

FIG. 6 illustrates a screen shot 600 of a hydration index 602 comparinga user's current hydration 606 to a baseline 612 of hydration for theuser. A chart or table 622 may all visualizing the user's hydration 606benchmarked against the baseline 612 for the user. The chart 622, invarious embodiments, may be adapted to show different granularities,e.g., to include trending data over several days, a week or more.

FIG. 7A depicts a base station and/or a UMD 710 configured tocommunicate data, such as input data, with one or more other devicessuch as a tablet computer 720, a smart phone 730, and/or other computingdevice 750 according to one embodiment. In various other embodiments,the other devices may be non-wearable and/or non-portable devices, suchas a bathroom scale or a bed scale 720, a medical device 730, and/or acontinuous positive airway pressure (CPAP) device 750. In anotherembodiment, the base station and/or the UMD may store and/or analyze thedata received from the one or more other devices separately from data ofthe base station and/or the UMD. In another embodiment, the base stationand/or the UMD may aggregate the data received from the one or moreother devices with the input data of the base station and/or the UMD. Inanother embodiment, the base station and/or the UMD may store,synchronize, and/or analyze the aggregated data of the one or more otherdevices and the base station and/or the UMD.

FIG. 7B illustrates a base station and/or a UMD 710 operable tocommunicate input data to a computing device 730, such as the hub 120 orserver 150 according to two embodiments. In one example, the basestation and/or the UMD 710 may communicate input data directly to thecomputing device 730 using a communications connection 750 of acommunications network. In another example, the base station and/or theUMD 710 may indirectly communicate the input data to the computingdevice 730 using another base station or another UMD 710 alongcommunication connections 760.

FIG. 7B further illustrates that the base station and/or a UMD 710 mayreceive selected data or information, such as input data or otherinformation, from the computing device 730. In one example, the basestation and/or a UMD 710 may receive selected data or information for auser of the base station and/or a UMD 710 from a cloud-based server or aserver in communication with a cloud-based server.

In one embodiment, the input data may include setting information forthe base station and/or a UMD 710. In one example, the settinginformation may include: measurement data threshold ranges, measurementdata threshold values, measurement event triggering values, and soforth. In another example, the input information may include: medicalinformation of the user of a UMD, user condition information, medicationregiment information, exercise regimen information, medical riskinformation, and so forth.

In another embodiment, the UMD 710 and/or the base station may provide asensory indication (such as a visual, auditory, and/or touch indication)communicating the selected data or information to the user. In oneexample, the UMD and/or the base station 710 may display a reminder fora user to exercise, take medication, rehydrate, and so forth.

In one embodiment, the base station may analyze received input dataand/or stored input data (such as measurement information) to determineselected states or conditions, such as medical conditions, physiologicalstates, and so forth of the user of the UMD. In another embodiment, thebase station may aggregate input data received from a plurality of UMDs.In another embodiment, the base station may aggregate current input datareceived from one or more UMD or other base station with previous inputdata stored at the base station or a device in communication with thebase station. In another embodiment, the base station may analyze theaggregated sync data.

In one configuration, the base station may communicate other informationto one or more UMDs. For example, the base station may receive softwareand/or firmware update information and relay the software and/orfirmware update to the one or more UMDs. In one embodiment, the basestation may communicate the other information to the one or more UMDswhen the one or more UMDs receive energy (such as wired energy orwireless energy) from the base station.

FIG. 8 is a flow chart 800 of an exemplary method for correlatingphysiological data to predict a change in a physiological parameter. Inone embodiment, the method includes receiving the first physiologicaldata and the second physiological data (810). The method may furtherinclude analyzing the first physiological data to determine a firstcorrelation between the first physiological data and a physiologicalparameter (820), and analyzing the second physiological data todetermine a second correlation between the second physiological data andthe physiological parameter (830). The method may also includepredicting a change in a level of the physiological parameter accordingto a combination of the first correlation and the second correlation(840). In just one example, the first physiological data includesoptical spectroscopy (or luminosity) levels, the second physiologicaldata skin impedance levels, and the physiological parameter includeshydration or activity level.

The first and second physiological data may be obtained from other thanthe physiological parameter. For example, if the physiological parameterto be predicted is hydration, the physiological data from which thecorrelation is made will not be direct hydration measurements, but couldbe, for example, measurements such as bio-impedance spectroscopy oroptical spectroscopy data that may relate to hydration. As anotherexample, if the physiological parameter to be predicted is electrolytelevels of an athlete, then the first and second physiological data maycome from other than direct electrolyte level measurements.

The embodiment of the method of FIG. 8 may further decide whetherenvironmental data is also available related to the physiologicalparameter (850). If not, the method may loop back to block 810. If yes,the method may receive environmental data including an environmentalparameter (860). The method may further include analyzing theenvironmental data to determine a third correlation between theenvironmental parameter and the physiological parameter (870). Themethod may then include predicting the change in the level of thephysiological parameter also according to the third correlation (880).

In one embodiment, the first physiological data and the secondphysiological data may be taken during a first time period, and thechange in the level of the physiological parameter is predicted for asecond period such as immediately following the first time period,corresponding to the first time period on a subsequent day or during afuture day while the user performs a similar or identical activity. Inthis way, the method may include analyzing the physiological data ofprevious time periods or during a current time period at which time acorrelation is made.

FIG. 9 is a flow chart 900 of an exemplary method for analyzinghistorical physiological data and environmental data to predict a changein a physiological parameter. The method may include receiving sensordata comprising physiological data and environmental data (910). Themethod may then include analyzing historical physiological data andenvironmental data (920) to determine a first correlation between afirst physiological parameter and a second physiological parameter (930)and a second correlation between an environmental parameter and thesecond physiological parameter (940). The method may also includepredicting a change in a level of the second physiological parameter ofan identified person for which the physiological data is received basedon the first correlation and the second correlation (950).

In one example, the second physiological parameter is hydration oroxygenation. The first physiological parameter may be something otherthan the second physiological parameter, including, heart rate, skintemperature, tissue bulk absorption, bio-impedance spectroscopy, orblood pressure. In one embodiment, the environmental data may betemperature, ambient humidity, altitude, geographical location, and/ortime of day. The historical physiological data may be of the identifiedperson or of a group of persons.

In some embodiments, the method may also determine that the firstcorrelation is below a threshold correlation for the first physiologicalparameter and disregard the first correlation when predicting the changein the level of the second physiological parameter. Additionally, oralternatively, the method may determine that the second correlation isbelow a threshold correlation for the environmental parameter anddisregard the second correlation when predicting the change in the levelof the second physiological parameter.

FIG. 10 is a flow chart 1000 of an exemplary method for setting abaseline for a physiological parameter based on a most-similar baselineprofile of another. The method may include receiving, and storing in amemory device as a user profile (1010). The profile information for auser may include gender, age, weight, health and fitness level, as wellas family health histories. The method may further include detectingprofile matches with baseline profiles of other users (1020). When thereare none, the method may loop back to block 1010. When there arematches, the method may further include identifying the baselineprofile, from all of the matching baseline profiles of the users, theone that is most similar to the user profile based on a correlationbetween the user profile information and baseline profile information(1030). The method may then set a baseline for measurements of aphysiological parameter of the user corresponding to levels of thephysiological parameter of an individual with the baseline profile thatis most-similar (1040). The user may then, based on measurements of thephysiological parameter, be informed (e.g., through a display, indicatoror graphical user interface) whether the measurements are above or belowthe baseline and by how much. In one example, those measurements may beaveraged over a period of time (such as an hour, a workout, or over aperiod of day(s)) before being compared with the baseline.

In additional embodiments, the baseline may later be updated based onupdated profile information, including trending changes in thephysiological parameter of the user, and/or on correlations betweenresearch information and measurements of the physiological parameter.

In one embodiment, to identify the baseline profile, the method mayinclude applying weights to a plurality of traits within the userprofile, to generate a plurality of weighted traits, and matching theweighted traits with corresponding levels of the weighted traits withinthe plurality of baseline profiles. Furthermore, in at least oneembodiment, the method may set the baseline for the physiologicalparameter as an average of the levels of the physiological parameter ofa determined number of individuals with most-similar profiles.

In applying weighting as disclosed herein, the processor 303 or 403 maybe adapted to dynamically weight certain data for a user, taking intoconsideration that a fitness level of the user may change over time, ametabolic rate of the user may change over time, a sweat rate of theuser may change over time, such as staying more hydrated with the sameamount of fluids, an average heart rate of the user may change overtime, and the like. In one embodiment, these types of measurements maybe weighted temporally, where more recent measurements are weighted morehighly than older measurements.

Alternatively, or additionally, these types of measurements may beweighted according to consistency of data based on analysis ofstatistical distribution of the measurements. For example, measurementdata that has little statistical variation may be weighted higher thandata with scattered statistical distribution.

Alternatively, or additionally, these types of measurements can beweighted according to outcome as measured against a known standard. Forexample, heart rate for a user can be measured against a target heartrate for users of the same age, and data from other users of the sameage group may be weighted higher for a comparison base. This sort ofoutcome weighting may be performed as a multivariate analysis of anumber of parameters weighted according to importance of outcome orcomparison with a known standard.

Alternatively, or additionally, these types of measurements may beweighted based on quality of the data, as discussed in the precedingparagraphs. For example, data with a higher signal-to-noise (SNR) orfrom a preferred, more reliable, source may be weighted higher than datawith a lower SNR or from a questionable source.

The present weighting methods, as described herein, may then be blendedto weave in different sources and types of data into a multifactoranalysis in which one or more of the above weighting schemes can beemployed. In one embodiment, these weightings are performed ascoefficients of multiple polynomials in an algorithm, formula orstatement that forms a part of an analysis regarding a user, generates auser profile, a diagnosis or a performance-based recommendation providedto a user, as just a few non-exhaustive examples.

FIG. 11 is a flow chart 1100 of an exemplary method for updating abaseline for a physiological parameter based on an update to profileinformation of a user. The method may include receiving updates toprofile information from a first user (1110). Such profile informationmay include gender, age, weight, health and fitness level, as well asfamily health history and the like. The method may further includegenerating an updated user profile for the first user based on theupdates (1120). The method may then detect profile matches of theupdated user profile with baseline profiles of other users that aremost-similar to the user's updated profile (1130). When there are nomatches sufficiently close (e.g., a threshold matching level) to theuser's updated profile, the method may loop back to block 1110. Whenthere are matches, the method may include identifying a second usercorresponding to a most-similar baseline profile to that of the updateduser profile (1140). The method may then update a baseline formeasurements of a physiological parameter of the user based on levels ofthe physiological parameter of the second user, to generate an updatedbaseline for the user (1150). The user may then, based on measurementsof the physiological parameter, be informed (e.g., through a display,indicator or graphical user interface) whether those measurements areabove or below the updated baseline and by how much. In some example,the measurements are averaged over a period of time.

In an alternative embodiment, the second user may be a group of users,and/or the levels of the physiological parameter of the group of usersmay be an average of the levels across individuals of the group ofusers. In such a case, the baseline may be set instead with respect tothe group of users.

FIG. 12 is a flow chart 1200 of an exemplary method for alerting a userthrough an indicator of a wearable UMD in anticipation of undertaking aphysical activity. The method may include receiving activity data for auser over time (1210). The method may further include analyzing theactivity data to predict when the user will be involved in a physicalactivity (1220). The method may further include alerting the userthrough the indicator to take an action in anticipation of the physicalactivity at a proper time before a start of the physical activity(1240). In one embodiment, the alert may be sent over a network betweena networked device and the UMD. The specific action may be actions suchas to hydrate, to eat, to rest or to warm up. The proper time may bedetermined by how far before the physical activity the user may need toperform the specific action (e.g., hydrating continuously starting 4 to8 hours before, eating at least 2 hours before, and warming up starting20-30 minutes prior to the physical activity), and which may becustomized by a trainer, coach, medical person or the user.

In some embodiments, the method includes machine learning habits of theuser involving differing activity levels of the user based on theanalyzed activity data over time. Through machine learning of thesehabits, the method may be able to predict what kind of activity the userundertakes depending on the day and time of day, and even in some cases,the period of time during the year.

The method may further include selecting from a plurality of indicatorsdepending on a type of the physical activity or the nature of thespecific action to be taken, e.g., selected from two or more of a light,a display, a speaker, a vibrator, and a touch screen.

In one embodiment, the activity data may include activity levels relatedto geographic locations of the user, and the method may further includedetermining a correlation between information from the activity data andthe physical activity, and identifying the physical activity as aprobable activity the user will undertake based on the correlation.

In another embodiment, the activity data may include scheduling dataincluding upcoming appointments of the user, where the method mayfurther include identifying additional information that indicates anappointment corresponding to the physical activity relates to anon-physical activity and adjusting the alert (sent in block 1240) tothe user to account for a likelihood that the appointment relates to anon-physical activity.

In an a further Example 1, a computing device may include 1) a memorydevice for computer storage; 2) a display having a user interface forengagement by a user; and 3) a processing device to: a) store, in thememory device, input data including environmental data and physiologicaldata, the physiological data received from a sensor in bodily engagementwith the user; b) analyze the input data to identify a trend in one ormore physiological parameters of the user; and c) display the input datain the user interface to alert the user of the trend.

In an Example 2, the computing device Example 1 may further include ahousing formed and shaped to affix to a user, wherein the sensor isintegrated into the housing such as to make contact with a body of theuser.

In an Example 3, the computing device of claim Example may furtherinclude a communication interface to send the input data to a networkeddevice over a network, for storage in the networked device.

In an Example 4, the computing device of Example 1, wherein the inputdata further includes an activity level of the user and the trendcomprises a first trend, wherein the processing device is further to: a)analyze the input data to identify a second trend in the activity level;b) analyze the input data to identify a third trend in an environmentalparameter; and c) display the input data in the user interface to alertthe user of the second trend or of the third trend.

In an Example 5, the computing device of Example 1, wherein theenvironmental data and physiological data includes, respectively, firstenvironmental data and first physiological data related to the user, andwherein the input data further includes second environmental data andsecond physiological data stored for a group of users.

In an Example 6, the computing device of Example 5, wherein theprocessing device is further to sort a plurality of environmentalparameters and physiological parameters from the first and secondphysiological data and from the first and second environmental data, theplurality of environmental parameters and physiological parameterscomprising any or a combination of: a time of day, a day of week, groupinformation, individual user information, a measurement type, ameasurement duration, an activity type, a piece of profile information,injury information, and performance information.

In an Example 7, the computing device of Example 5, wherein the inputdata further includes user-specific profile information for the group ofusers, and the processing device is further to: a) index the input datafor the group of users at a group level to generate a group database; b)index individual user input data on an individual level to generate aplurality of individual databases; and c) make the group database andthe plurality of individual databases searchable through the userinterface.

FIG. 13 provides an example illustration of a processing devicedisclosed herein, such as a user equipment (UE), a base station, a UMD,a mobile wireless device, a mobile communication device, a tablet, ahandset, or other type of wireless device according to one embodiment.The device may include one or more antennas configured to communicatewith a node or transmission station, such as a base station (BS), anevolved Node B (eNode B), a baseband unit (BBU), a remote radio head(RRH), a remote radio equipment (RRE), a relay station (RS), a radioequipment (RE), a remote radio unit (RRU), a central processing module(CPM), or other type of wireless wide area network (WWAN) access point.The device may be configured to communicate using at least one wirelesscommunication standard including 3GPP LTE, WiMAX, High Speed PacketAccess (HSPA), Bluetooth, and Wi-Fi. The device may communicate usingseparate antennas for each wireless communication standard or sharedantennas for multiple wireless communication standards. The device maycommunicate in a wireless local area network (WLAN), a wireless personalarea network (WPAN), and/or a WWAN.

FIG. 13 also provides an illustration of a microphone and one or morespeakers that may be used for audio input and output from the device.The display screen may be a liquid crystal display (LCD) screen, orother type of display screen such as an organic light emitting diode(OLED) display. The display screen may be configured as a touch screen.The touch screen may use capacitive, resistive, or another type of touchscreen technology. An application processor and a graphics processor maybe coupled to internal memory to provide processing and displaycapabilities. A non-volatile memory port may also be used to providedata input/output options to a user. The non-volatile memory port mayalso be used to expand the memory capabilities of the wireless device. Akeyboard may be integrated with the wireless device or wirelesslyconnected to the wireless device to provide additional user input. Avirtual keyboard may also be provided using the touch screen.

Various techniques, or certain aspects or portions thereof, may take theform of program code (i.e., instructions) embodied in tangible media,such as floppy diskettes, CD-ROMs, hard drives, non-transitory computerreadable storage medium, or any other machine-readable storage mediumwherein, when the program code is loaded into and executed by a machine,such as a computer, the machine becomes an apparatus for practicing thevarious techniques. In the case of program code execution onprogrammable computers, the computing device may include a processor, astorage medium readable by the processor (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device. The volatile and non-volatile memoryand/or storage elements may be a RAM, EPROM, flash drive, optical drive,magnetic hard drive, or other medium for storing electronic data. Thebase station and mobile station may also include a transceiver module, acounter module, a processing module, and/or a clock module or timermodule. One or more programs that may implement or utilize the varioustechniques described herein may use an application programming interface(API), reusable controls, and the like. Such programs may be implementedin a high level procedural or object oriented programming language tocommunicate with a computer system. However, the program(s) may beimplemented in assembly or machine language, if desired. In any case,the language may be a compiled or interpreted language, and combinedwith hardware implementations.

It should be understood that many of the functional units described inthis specification have been labeled as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom VLSIcircuits or gate arrays, off-the-shelf semiconductors such as logicchips, transistors, or other discrete components. A module may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices or thelike.

Modules may also be implemented in software for execution by varioustypes of processors. An identified module of executable code may, forinstance, comprise one or more physical or logical blocks of computerinstructions, which may, for instance, be organized as an object,procedure, or function. Nevertheless, the executables of an identifiedmodule need not be physically located together, but may comprisedisparate instructions stored in different locations which, when joinedlogically together, comprise the module and achieve the stated purposefor the module.

Indeed, a module of executable code may be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different programs, and across several memory devices.Similarly, operational data may be identified and illustrated hereinwithin modules, and may be embodied in any suitable form and organizedwithin any suitable type of data structure. The operational data may becollected as a single data set, or may be distributed over differentlocations including over different storage devices, and may exist, atleast partially, merely as electronic signals on a system or network.The modules may be passive or active, including agents operable toperform desired functions.

Reference throughout this specification to “an example” means that aparticular feature, structure, or characteristic described in connectionwith the example is included in at least one embodiment of the presentinvention. Thus, appearances of the phrases “in an example” in variousplaces throughout this specification are not necessarily all referringto the same embodiment.

As used herein, a plurality of items, structural elements, compositionalelements, and/or materials may be presented in a common list forconvenience. However, these lists should be construed as though eachmember of the list is individually identified as a separate and uniquemember. Thus, no individual member of such list should be construed as ade facto equivalent of any other member of the same list solely based ontheir presentation in a common group without indications to thecontrary. In addition, various embodiments and example of the presentinvention may be referred to herein along with alternatives for thevarious components thereof. It is understood that such embodiments,examples, and alternatives are not to be construed as defactoequivalents of one another, but are to be considered as separate andautonomous representations of the present invention.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments. In theforegoing description, numerous specific details are provided, such asexamples of layouts, distances, network examples, etc., to provide athorough understanding of embodiments of the invention. One skilled inthe relevant art will recognize, however, that the invention may bepracticed without one or more of the specific details, or with othermethods, components, layouts, etc. In other instances, well-knownstructures, materials, or operations are not shown or described indetail to avoid obscuring aspects of the invention.

While the foregoing examples are illustrative of the principles of thepresent invention in one or more particular applications, it will beapparent to those of ordinary skill in the art that numerousmodifications in form, usage and details of implementation may be madewithout the exercise of inventive faculty, and without departing fromthe principles and concepts of the invention. Accordingly, it is notintended that the invention be limited, except as by the claims setforth below.

FIG. 14 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 1400 within which a set ofinstructions for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeimplementations, the machine may be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client machine in aclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The exemplary computer system 1400 includes a processing device(processor) 1402, a main memory 1404 (e.g., read-only memory (ROM),flash memory, dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 1406 (e.g.,flash memory, static random access memory (SRAM), etc.), and a datastorage device 1418, which communicate with each other via a bus 1430 orthrough another means such as a communication interface and/or directconnections.

Processing device 1402 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1402 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1402 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a field programmable gate array (FPGA), a digital signalprocessor (DSP), network processor, or the like. The processing device1402 is configured to execute instructions 1426 for performing theoperations and steps discussed herein.

The computer system 1400 may further include a network interface device1408. The computer system 1400 also may include a video display unit1410 (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), ora touch screen), an alphanumeric input device 1412 (e.g., a keyboard), acursor control device 1414 (e.g., a mouse), and a signal generationdevice 1416 (e.g., a speaker or other indictor(s)). The computer system1400 may further include a graphics processing unit 1422, a videoprocessing unit 1428 and an audio processing unit 1432.

The data storage device 1418 may include a machine-readable storagemedium 1424 on which is stored one or more sets of instructions 1426(e.g., software) embodying any one or more of the methodologies orfunctions described herein. The instructions 1426 may also reside,completely or at least partially, within the main memory 1404 and/orwithin the processing device 1402 during execution thereof by thecomputer system 1400, the main memory 1404 and the processing device1402 also constituting computer-readable storage media. The instructions1426 may further be transmitted or received over the communicationsnetwork 115 via the network interface device 1408.

In one example, the communications network 115 may be a cellular networkthat may be a third generation partnership project (3GPP) release 8, 9,10, 11, or 12 or Institute of Electronics and Electrical Engineers(IEEE) 802.16p, 802.16n, 802.16m-2011, 802.16h-2010, 802.16j-2009,802.16-2009. In another embodiment, communications network may be awireless network (such as a wireless local area network (e.g., networkusing Wi-Fi® technology) that may follow a standard such as the IEEE802.11-2012, IEEE 802.11ac, or IEEE 802.11ad standard. In anotherembodiment, the communications network may be a PAN connection (e.g., aconnection using Bluetooth® technology) such as Bluetooth® v1.0,Bluetooth® v2.0, Bluetooth® v3.0, or Bluetooth v4.0. In anotherembodiment, the communications network may be a PAN connection (e.g., aconnection using the Zigbee® technology), such as IEEE 802.15.4-2003(Zigbee® 2003), IEEE 802.15.4-2006 (Zigbee® 2006), IEEE 802.15.4-2007(Zigbee® Pro). In one embodiment, the base station and the UMD may usenear field communication, or induction communication to communicateinformation between the base station and the UMD.

While the machine-readable storage medium 1424 is shown in an exemplaryimplementation to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description have been presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “segmenting”, “analyzing”, “determining”, “enabling”,“identifying,” “modifying” or the like, refer to the actions andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical (e.g.,electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may include a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment” in various places throughout thisspecification are not necessarily all referring to the same embodiment.In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.”

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other implementations will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A wearable device comprising: a housing formedand shaped to affix to a user; a sensor integrated into the housing andto engage a body of the user to take physiological measurements of theuser to obtain first physiological data and second physiological data; aprocessing device located within the housing and to: receive the firstphysiological data and the second physiological data; analyze the firstphysiological data to determine a first correlation between the firstphysiological data and a physiological parameter; analyze the secondphysiological data to determine a second correlation between the secondphysiological data and the physiological parameter; and predict a changein a level of the physiological parameter according to a combination ofthe first correlation and the second correlation.
 2. The wearable deviceof claim 1, wherein the sensor comprises a sensor array including afirst sensor to obtain the first physiological data and a second sensorto obtain the second physiological data, and wherein the firstphysiological data and the second physiological data are derived fromother than the physiological parameter.
 3. The wearable device of claim1, wherein the first physiological data comprises optical spectroscopylevels, the second physiological data comprises bio-impedancespectroscopy levels, and wherein the physiological parameter compriseshydration.
 4. The wearable device of claim 1, wherein the processingdevice is further to: receive environmental data including anenvironmental parameter; analyze the environmental data to determine athird correlation between the environmental parameter and thephysiological parameter; and predict the change in the level of thephysiological parameter also according to the third correlation.
 5. Thewearable device of claim 4, wherein the environmental parametercomprises any or a combination of: ambient temperature, ambienthumidity, altitude, geographical location, and time of day.
 6. Thewearable device of claim 1, wherein the processing device is further tofilter out one of the first correlation or the second correlationresponsive to a level of correlation thereof being below a thresholdlevel of correlation.
 7. The wearable device of claim 1, wherein thefirst physiological data or the second physiological data comprises anyor a combination of: oxygenation levels, heart rate levels, bloodpressure levels, bio-impedance spectroscopy levels, and opticalspectroscopy levels.
 8. The wearable device of claim 1, wherein thefirst physiological data and the second physiological data are takenduring a first time period, and the change in the level of thephysiological parameter is predicted for a second period including atime period comprising one of: immediately following the first timeperiod; corresponding to the first time period on a subsequent day; orduring a future day while the user performs a similar or identicalactivity.
 9. An apparatus comprising: a processing device; anon-transitory computer-readable medium coupled to the processingdevice, the non-transitory computer-readable medium to storeinstructions and data; the processing device to execute the instructionsto: receive sensor data comprising physiological data and environmentaldata; analyze historical physiological data and environmental data todetermine: a first correlation between a first physiological parameterand a second physiological parameter; and a second correlation betweenan environmental parameter and the second physiological parameter; andpredict a change in a level of the second physiological parameter of anidentified person for which the physiological data is receivedresponsive to the first correlation and the second correlation.
 10. Theapparatus of claim 9, further comprising a sensor comprising any or acombination of: a bio-impedance spectrometer, an optical sensor, asingle wire electrocardiogram (ECG), a temperature sensor, and athree-dimensional accelerometer.
 11. The apparatus of claim 9, whereinthe second physiological parameter comprises hydration or oxygenation.12. The apparatus of claim 11, wherein the first physiological parametercomprises any or a combination of oxygenation, heart rate, bio-impedancespectroscopy, tissue bulk absorption, skin temperature and bloodpressure.
 13. The apparatus of claim 9, wherein the environmental datacomprises any or a combination of: ambient temperature, ambienthumidity, altitude, geographical location, and time of day.
 14. Theapparatus of claim 9, wherein the historical physiological data is ofthe identified person.
 15. The apparatus of claim 9, wherein thehistorical physiological data is of a group of persons.
 16. Theapparatus of claim 9, wherein the processing device is further to:determine that the first correlation is below a threshold correlationfor the first physiological parameter; and disregard the firstcorrelation when predicting the change in the level of the secondphysiological parameter.
 17. The apparatus of claim 9, wherein theprocessing device further to: determine that the second correlation isbelow a threshold correlation for the environmental parameter; anddisregard the second correlation when predicting the change in the levelof the second physiological parameter.
 18. The apparatus of claim 9,wherein the processing device further to: assign a first weight to thefirst correlation responsive to a type of the first physiologicalparameter; and assign a second weight to the second correlationresponsive to a type of the environmental parameter.
 19. A methodcomprising: receiving, during a first period, first physiological dataand second physiological data from a sensor engaging a body of a user;analyzing, using at least one processing device, the first physiologicaldata to determine a first correlation between the first physiologicaldata and a physiological parameter; analyzing, using the at least oneprocessing device, the second physiological data to determine a secondcorrelation between the second physiological data and the physiologicalparameter; and predicting, using the at least one processing device, achange in a level of the physiological parameter during a second timeperiod according to a combination of the first correlation and thesecond correlation, wherein the first physiological data and the secondphysiological data exclude the physiological parameter.
 20. The methodof claim 19, wherein the second time period comprises a time period:following the first time period during an identical day; correspondingto the first time period on a subsequent day; or during a future daywhile the user performs a similar or identical activity.
 21. The methodof claim 19, wherein the first physiological data comprises an averagetissue bulk absorption, the second physiological data comprises anaverage bio-impedance spectroscopy, and wherein the physiologicalparameter comprises hydration.
 22. The method of claim 19, furthercomprising: determining that the first correlation is below a thresholdcorrelation for the first physiological data; and disregarding the firstcorrelation when predicting the change in the level of the physiologicalparameter.
 23. The method of claim 19, further comprising: receivingenvironmental data including an environmental parameter; analyzing theenvironmental data to determine a third correlation between theenvironmental parameter and the physiological parameter; and predictingthe change in the level of the physiological parameter also according tothe third correlation.
 24. The method of claim 23, further comprising:determining that the third correlation is below a threshold correlationfor the environmental parameter; and disregarding the third correlationwhen predicting the change in the level of the physiological parameter.25. The method of claim 23, further comprising: assigning a first weightto the first correlation according to a type of the first physiologicaldata; assigning a second weight to the second correlation according to atype of the second physiological data; and assigning a third weight tothe third correlation according to a type of the environmentalparameter.